Quality is king: how to pick the best data for your salary benchmarking and compensation decisions

December 13, 2023
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Quality is king: how to pick the best data for your salary benchmarking and compensation decisions

All compensation data is not created equal. With payroll being one of the largest costs for most companies, using "bad data" can lead to negative consequences for your organization.

Learn how to evaluate and find the best data to make the best compensation decisions and benchmark salaries:

  • The importance of quality data in compensation
  • Ultimate compensation data quality checklist

The importance of quality data in compensation

First off let's go over some key concepts in employee compensation & salary benchmarking:

  • Salary benchmarking: This is a process of comparing the compensation practices of an organization with that of the industry standards. This helps in ensuring that the organization is offering competitive salaries to its employees.
  • Employee retention: Retaining skilled and talented employees is essential for any organization to achieve its goals. Offering competitive salaries and benefits is one way to retain employees.
  • Market changes: The job market is dynamic, and changes can occur frequently. Organizations need to keep up with these changes to remain competitive in attracting and retaining employees.

In order to make the very best compensation decisions, your responsibility is to ensure that the source of data is quality. 

What is bad data? 

Bad data is inaccurate, incomplete, or outdated information that can lead to wrong decisions.

In the context of compensation, bad data can lead to the following:

  • Overpaying or underpaying employees
  • Losing top talent to competitors
  • Decreasing employee morale and engagement

Need an example? How about Glassdoor?

Now we have identified how bad data is harmful to your company, let's dive into how you can evaluate and find good data...

What is good data?

Quality data sources are characterized by the following:

  • Accuracy: The data should be up-to-date, reliable, and relevant to your organization.
  • Reputation: The source should have a good reputation in the industry and be known for providing accurate and reliable data.
  • Transparency: The source should be transparent about their data collection methodologies.

Ultimate compensation data quality checklist

To help you evaluate the quality of your data source, use this comprehensive checklist.

#1 Are the data sources transparent?

Does your compensation data platform share the following:

  • How many datapoints are collected?
  • When the data was last collected?
  • What industries and location the data came from?
Figures is fully transparent about the number of datapoints from each location. As of April 2023: 80k+ datapoints from 1.2k+ companies

#2 Is the data verified for accuracy?

Can you easily find or get answers to the following questions:

  • What is the source of the data? 
  • If there is an onboarding process, is the mapping done correctly? 
  • How is the data verified? 

Figures has a team of expert account managers who verify & provide support to any of your questions.

#3 Is there a trust level displayed & explained? 

You want to use salary data that you can trust. 

But what makes data trustworthy?

  • The sample size
  • Transparency on confidence level of the data

If your salary solution doesn't have either of those readily available, then be wary of if you can really trust the data.

Figures displays the level of trust of EACH of your salary benchmark queries so you can be confident in your decision.

In conclusion...

Although it may seem like you can use data from any source for your compensation decisions, in reality the QUALITY of the data is extremely important.

That's why it's important to find data sources that are reliable, accurate, and transparent about how they collect their data.

Like Figures, get in touch with our team today to get access to our secure, trustworthy dataset. Over 1,200 top companies in Europe and the UK use us, why not you? 

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